Capturing Cancer Evidence

The Clinical Case

In June 2017, Basch E et al published randomised controlled trials evidence of the benefits of using a digital platform to remotely measure patient-reported outcome measures (PROMs) – clinical assessments that capture the impact of a condition on a patient’s life at a time point – alongside the usual processes of care for patients undergoing chemotherapy for advanced cancer. The study group were found to survive an average of five months longer (which translated to an absolute five-year survival benefit of 8%) than the control group. Additionally, 31% reported quality of life benefits at six months, and emergency room visits were 7% lower (1).

These surprising results were better than those reported by the majority of recently licensed chemotherapy drugs, were more impactful on quality of life, and contributed positively to overall care efficiency and cost effectives.

The reasons for the benefits are because of how the technology allowed the service to better meet the needs of patients. Using remotely collected symptom and outcome data, specialist nurses could monitor patients between appointments and look for treatable problems that may otherwise have escalated and mean patients could no longer tolerate a hospital visit for further doses of chemotherapy, or worse, deteriorate to the point that they are admitted to hospital and deteriorate further.

Dr Tim Williams is the Co-founder and CEO of My Clinical Outcomes – a technology platform that automates the collection and analysis of PROMs in clinical practice. Tim previously worked as an NHS doctor and strategy consultant and has a keen interest in how technology can help improve healthcare services, having also undertaken Professor Michael Porter’s Value-Based Healthcare seminar series at Harvard Business School, US.

Because a key step towards database lock is obtaining clean data for all patients, the ability to verify that data are clean early accelerates the ability to lock the clinical database. This ultimately shortens the overall study timeline while ensuring the quality of your data. However, this can be challenging when manually reviewing study data, particularly with larger studies.
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